「reviews」の検索結果

Analyze liquid markets with a strict ICT/SMC framework that combines multi-timeframe bias, liquidity sweeps, displacement, close-confirmed MSS/BOS, FVG/OB/Breaker/OTE retracements, premium-discount location, session timing, invalidation, targets, and risk-to-reward. Use for ICT chart reviews, live-market analysis, OHLCV scans, or validating a user's trade thesis. Return an auditable setup status and conditional plan; do not use for generic indicator analysis, order execution, or claims of proven institutional intent.

日本語の概要は準備中です。原文の説明を表示しています。

End-to-end 3D printing for Bambu Lab printers. Finds models online (MakerWorld, Printables), generates them with AI (text-to-3D, image-to-3D) or as exact-dimension parametric CAD, checks and repairs printability, converts textures to AMS multi-color, renders previews, opens them in Bambu Studio for the user to print, and reads printer status, AMS filaments and print progress. Use this whenever the user wants to 3D print something, design or model an object for printing, work with STL/3MF/OBJ/GLB files for a printer, check on a Bambu Lab printer (A1, A1 Mini, A2L, P1S, P2S, X1C, X1E, X2D, H2C, H2S, H2D, H2D Pro), or asks about AMS filament, slicing or print progress, even if they don't say "Bambu".

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks to run or continue an autonomous build: turn a goal into an approved plan, dispatch implementation and review agents, and finish with one pull request. The Lead is a provider-neutral orchestrator that makes decisions but never writes code, edits files, runs commands, or reviews its own work.

日本語の概要は準備中です。原文の説明を表示しています。

Perform thorough code reviews on files or pull requests, checking for bugs, security vulnerabilities, performance issues, and style violations. Use when the user requests code review or provides relevant inputs for this workflow.

日本語の概要は準備中です。原文の説明を表示しています。

Think and work like an expert Dentist-Scientist. Use when a task calls for Dentist-Scientist judgment. Reasons from oral biofilm-host ecology, tissue healing capacity, and patient-level clinical endpoints (DMFS, PD/CAL, implant survival) through PICO/PROSPERO protocols, CAMBRA and 2017 AAP/EFP staging, ISO 4049/14801 bench tests with thermocycling, and GRADE-rated reviews while treating in-vitro-to-chairside leaps, plaque-index surrogates without caries reduction, and examiner calibration drift as first-class failure modes.

日本語の概要は準備中です。原文の説明を表示しています。

Use this skill when producing speech, narration, dubbing, localization, advertising voice, voice-clone previews, or interactive voice with MiniMax speech/audio APIs. It covers MiniMax T2A HTTP, WebSocket streaming, async long-form TTS, Speech 2.8/2.6/02 model selection, system and custom voices, rapid voice cloning, voice design, pronunciation/language/emotion controls, pricing and limits, artifact custody, consent and rights checks, and production QA.

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Provider-independent cinematic trailer production for AI agents creating movie-style trailers, teaser trailers, launch trailers, game trailers, documentary trailers, series promos, and high-impact generated-media previews. Use for trailer strategy, structure, shot planning, voiceover/title-card copy, music and sound-design direction, generated-video prompt translation, edit pacing, platform/runtime choices, delivery handoff, rights/claims caveats, and trailer QA.

日本語の概要は準備中です。原文の説明を表示しています。

Provider-independent character design and continuity direction for generated media. Use when an agent must create, preserve, revise, or QA a recurring character across AI-generated images, video clips, avatars, animation, comics, ads, product mascots, serialized social posts, or multi-shot campaigns; especially when building character bibles, reference packs, prompts, expression/pose ranges, costume/hair/makeup/prop continuity, identity and likeness safeguards, iteration plans, handoff notes, or continuity reviews.

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Aggregate and analyze product and business reviews from Google Maps, Amazon, Akta, and Xiaohongshu. Cross-platform review intelligence including rating patterns, common themes, competitive comparison, and customer satisfaction trends.

日本語の概要は準備中です。原文の説明を表示しています。

Extract and analyze product reviews from e-commerce and review sites. Aggregates ratings, identifies common themes in positive and negative feedback, and surfaces actionable product insights from customer voices across platforms.

日本語の概要は準備中です。原文の説明を表示しています。

Research and collect social proof — testimonials, case studies, user reviews, social mentions, and community endorsements across platforms. Useful for marketing teams building trust content, landing pages, and sales collateral from authentic customer voices.

日本語の概要は準備中です。原文の説明を表示しています。

Analyze sentiment in any text — reviews, social posts, feedback, or articles. Returns sentiment polarity (positive, negative, neutral), confidence score, and key phrases driving the sentiment. Useful for brand monitoring, customer feedback analysis, and content evaluation.

日本語の概要は準備中です。原文の説明を表示しています。

Research local markets by combining Google Maps reviews, Xiaohongshu local content, Weibo discussions, and web search. Covers customer sentiment by location, local competition analysis, neighborhood trends, and location-specific brand perception.

日本語の概要は準備中です。原文の説明を表示しています。

Research products across Amazon, Google Shopping, and Xiaohongshu to understand pricing, reviews, market positioning, and consumer sentiment. Covers competitive product analysis, pricing benchmarks, feature comparison, and customer satisfaction signals.

日本語の概要は準備中です。原文の説明を表示しています。

Run outcome-focused Firecrawl workflows that produce deliverables such as research reports, literature reviews over published papers, SEO audits, QA reports, lead lists, knowledge bases, website design systems, and other structured web-data artifacts. Use when the user wants Firecrawl to complete a business, marketing, product, or creative workflow rather than merely scrape a page or integrate API calls into code.

日本語の概要は準備中です。原文の説明を表示しています。

Research products across the web with Firecrawl and produce a shopping recommendation or cart-ready summary. Use when the user wants to compare products, find the best option, evaluate reviews, respect budget/preferences, or shop with a saved browser session.

日本語の概要は準備中です。原文の説明を表示しています。

Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes: Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/spec asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use when the user wants to review a branch, a PR, work-in-progress changes, or asks to "review since X".

日本語の概要は準備中です。原文の説明を表示しています。

Analyze escaped defects and test suite health through blameless postmortems. Covers bug pattern analysis, test suite health reviews, 5 Whys root cause analysis, process improvement cycles, and postmortem/retro meeting templates with action item tracking. Use when: "QA retro," "escaped bugs," "postmortem," "quality incident," "defect analysis," "improvement cycle." Not for: live release go/no-go decisions — use release-readiness. Not for ongoing metric dashboards — use qa-metrics. Not for reviewing existing test code quality — use ai-qa-review. Related: qa-metrics, test-reliability, test-strategy.

日本語の概要は準備中です。原文の説明を表示しています。

Use AI to write NEW test code from specs, PRDs, user stories, code diffs, bug reports, or OpenAPI specs. Staged pipeline: requirements extraction → risk analysis → coverage matrix → scenario generation → oracle design → test code → human review, with guardrails against hallucinated APIs and weak assertions. Use when: "generate tests from spec," "tests from PRD," "tests from user story," "auto-generate test cases," "AI write tests for me." Not for: testing AI/LLM features in your product — use ai-system-testing. Not for: auditing a pre-existing test suite you did not just generate — use ai-qa-review (Step 7 here only reviews tests THIS pipeline produced). Related: playwright-automation, unit-testing, api-testing, qa-project-context.

日本語の概要は準備中です。原文の説明を表示しています。

Reviews and cleans up outdated documentation. Use periodically to maintain documentation health. Triggers on: review docs, clean up documentation, check for stale docs.

日本語の概要は準備中です。原文の説明を表示しています。

Reviews and adjusts PRD plans based on research findings. Use after completing research to evaluate story modifications. Triggers on: review plan, adjust stories, update prd based on research.

日本語の概要は準備中です。原文の説明を表示しています。

Generate a structured implementation plan from an evidence draft. Validate paths, obtain configured independent Codex and Qoder reviews, synthesize available advice against repository evidence, preserve the draft, and produce testable acceptance criteria and validation steps.

日本語の概要は準備中です。原文の説明を表示しています。

Design an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scales rigor to the task, runs a hypothesis loop, and logs decisions via show-me-your-work. Use for /figure-it-out, 'figure it out', a large migration, or when no narrower playbook applies.

日本語の概要は準備中です。原文の説明を表示しています。

Keep a reviewable decision trail for long-running or unattended work: a TSV log with one row per decision (what, why, evidence, result). Local by default; commit it when a reviewer needs the trail to trust the result. Use for /show-me-your-work, autonomous or multi-phase runs, or work a human reviews after stepping away.

日本語の概要は準備中です。原文の説明を表示しています。